System and method for joint estimation of attenuation and activity information

ABSTRACT

Imaging system and method are presented. Emission scan (ES) and anatomical scan (AS) data corresponding to a target volume in a subject are received. One or more at least partial AS images are reconstructed using AS data. An image-space certainty (IC) map representing a confidence level (CL) for attenuation coefficients of selected voxels in AS images and a preliminary attenuation (PA) map based on AS images are generated. One or more of selected attenuation factors (AF) in projection-space are initialized based on PA map. A projection-space certainty (PC) map representing CL for the selected AF is generated based on IC map. An emission image of the target volume is initialized. The selected AF and emission image are iteratively updated based on the ES data, PC map, initial AF, and/or initial emission image. A desired emission image and/or AF values are determined based on the iteratively updated AF and/or emission image.

BACKGROUND

Embodiments of the present specification relate generally to diagnosticimaging, and more particularly to methods and systems for jointestimation of attenuation and activity information.

Positron emission tomography (PET) finds use in generating images thatrepresent a distribution of positron-emitting nuclides, for example,within a patient's body. Accordingly, during PET imaging, a radionuclideis injected into the patient. As the radionuclide decays, positrons areemitted that collide with electrons, thereby resulting in anannihilation event. The annihilation converts the entire mass of thepositron-electron pair into two 511 kilo-electron volt (keV) photonsemitted in substantially opposite directions along a line of response(LOR). The PET system includes one or more detectors that are placedalong the LOR on a detector ring to detect the annihilation photons.Particularly, the detectors detect a coincidence event if the photonsarrive and are detected at the detector elements within a coincidencetime window. Subsequently, the PET system uses the detected coincidenceinformation along with other acquired image data for ascertaininglocalized concentrations of the radionuclide for use in generating afunctional diagnostic image.

However, during imaging, the photon-electron interactions may result inattenuation of emitted photons, which in turn, may lead to inaccuratePET quantitation and/or degraded image quality. Accordingly, certain PETimaging approaches are drawn to joint estimation of PET attenuation andactivity or emission maps from PET emission scan data, where allvoxels/pixels are initially unknown. However, the conventional jointestimation approaches may result in cross-talk artifacts and incorrectscaling due to an under-determinedness and ill-conditionedness of thecorresponding inverse problem, thus leading to incorrect PET attenuationcorrection.

Accordingly, PET imaging is often combined with an external radioactivesource to measure attenuation factors in projection-space or toreconstruct an attenuation map in image-space that is representative ofa spatial distribution of linear attenuation coefficients for theemission photons. Alternatively, an attenuation map is obtained fromanatomical scan data acquired using an anatomical imaging scanner. Forexample, in conventional emission tomography systems, PET imaging may becombined with computed tomography (CT) or magnetic resonance imaging(MRI) to correct for the photon attenuations.

Although CT may produce anatomical transmission data of desiredstatistical quality, CT imaging provides limited soft-tissue contrastand involves administering substantial radiation to a patient.Accordingly, in certain imaging scenarios, MRI may be used inconjunction with PET imaging for generating high-quality images for usein providing efficient diagnosis and/or treatment to a patient. To thatend, MRI and PET scans may be performed sequentially in separatescanners or simultaneously in a combined PET/MRI scanner. Particularly,simultaneous acquisition of PET and MRI data provides uniqueopportunities to study biochemical processes through fusion ofcomplementary information determined using the orthogonal MRI and PETimaging modalities.

MRI, however, may not provide a direct transformation of magneticresonance (MR) images into PET attenuation values. Generally, the MRimages reflect distribution of hydrogen nuclei with relaxationproperties rather than electron density, which is related to PETattenuation. Accordingly, certain conventional imaging approaches employsegmentation or atlas-based registration of the MR images to produce acorresponding patient-specific attenuation map. The attenuation map isthen forward-projected to determine attenuation factors, which in turn,are used to reconstruct corresponding PET activity or emission images.Use of MRI information, thus, enhances PET attenuation correction andthe subsequent PET image reconstruction.

However, the MRI information provides insufficient distinction betweenregions including lungs, air, bone, and/or metal even though theseconstituent materials have substantially different PET attenuationvalues. Accordingly, use of conventional segmentation and/or atlas-basedapproaches for estimation of PET attenuation maps using MRI informationmay result in inaccurate attenuation correction, particularly in and/ornear metal, bones, and lungs, subsequently leading to PET activityquantitation errors. Particularly, the atlas-based approaches may beunable to address significant inter-patient variations in anatomyparticularly for patient body parts other than heads. Moreover, incertain scenarios, MRI may provide only a truncated field of view (FOV)and may not suitably account for presence of extra-patient componentssuch as beds and coils in the vicinity. The truncated FOV and theextra-patient components may also contribute to photon attenuation, inturn, leading to inaccurate PET quantitation and/or degraded imagequality.

Accordingly, there is a need for a method and a system that mitigate theshortcomings of these conventional approaches to provide accurateattenuation correction in emission scan data. Particularly, it would bedesirable to design a method and a system that address insufficiencyand/or inaccuracy of MR-based attenuation images to provide efficientestimation of the attenuation values in and/or near conventionallyunclassifiable regions such as metal, air pockets, bones, and lungs,thereby allowing for accurate PET quantitation and image reconstruction.

BRIEF DESCRIPTION

In accordance with one aspect of the present specification, a method, asystem, and a non-transitory computer readable medium that storesinstructions executable by one or more processors to perform a methodfor imaging a subject is presented. The method includes receivingemission scan data and anatomical scan data corresponding to the targetvolume in the subject. Further, the method includes reconstructing oneor more at least partial anatomical scan images using the anatomicalscan data. Additionally, the method includes generating an image-spacecertainty map that represents a level of confidence corresponding to anattenuation coefficient for one or more selected voxels in the one ormore at least partial anatomical scan images. Moreover, the methodincludes generating a preliminary attenuation map based on the one ormore at least partial anatomical scan images. Further, the methodincludes initializing one or more of a selected set of attenuationfactors in a projection-space corresponding to the emission scan databased on the preliminary attenuation map. Additionally, the methodincludes generating a projection-space certainty map corresponding tothe emission scan data that represents a level of confidencecorresponding to a value of one or more of the selected set ofattenuation factors based on the image-space certainty map. The methodalso includes initializing an emission image corresponding to the targetvolume. Furthermore, the method includes iteratively updating one ormore of the selected attenuation factors and the emission image based onthe emission scan data, the projection-space certainty map, the initialattenuation factors, and/or the initial emission image. Moreover, themethod includes determining a desired emission image and/or desiredvalues of the attenuation factors based on the iteratively updatedattenuation factors and the iteratively updated emission image.

DRAWINGS

These and other features and aspects of embodiments of the presenttechnique will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a schematic view of an embodiment of a hybrid imaging systemconfigured to obtain attenuation-corrected emission images, inaccordance with an aspect of the present specification;

FIG. 2 is a flowchart depicting an exemplary method for enhancedtomographic imaging of a target volume in a subject, in accordance withaspects of the present specification;

FIGS. 3 and 4 illustrate transaxial and coronal slices of an embodimentof a true emission image, respectively;

FIGS. 5 and 6 illustrate transaxial and coronal slices corresponding toan embodiment of a true attenuation map, respectively;

FIG. 7 illustrates an exemplary image that depicts a plurality of thetrue attenuation factors;

FIGS. 8 and 9 illustrate transaxial and coronal slices of an preliminaryattenuation map determined from the MRI data, respectively;

FIG. 10 illustrates an exemplary image that depicts initial attenuationfactors;

FIGS. 11 and 12 illustrate transaxial and coronal slices, respectively,of an emission image that correspond to reference reconstructions of thetarget volume using the true attenuation map as shown in FIGS. 5 and 6;

FIGS. 13 and 14 illustrate transaxial and coronal slices, respectively,of an emission image reconstructed from noisy emission scan data usingthe preliminary attenuation map as shown in FIGS. 8 and 9;

FIGS. 15 and 16 illustrate transaxial and coronal slices, respectively,of an emission image reconstructed using an embodiment of the presentmethod described with reference to FIG. 2; and

FIG. 17 illustrates the final updated attenuation factors obtained usingthe method described with reference to FIG. 2.

DETAILED DESCRIPTION

The following description presents exemplary systems and methods forenhanced tomographic imaging using emission and anatomical scan data.Particularly, embodiments illustrated hereinafter disclose a hybridPET/MRI system and method that allow for simultaneous PET attenuationcorrection and PET image reconstruction via selective update ofconventionally ambiguous image-regions using acquired PET and MRIinformation.

Although exemplary embodiments of the present systems and methods aredescribed in the context of correction of PET and/or PET/MR images, theembodiments described herein are also applicable to attenuationcorrection and/or image modification in other modalities. For example,the disclosed embodiments may be used in other imaging systems such asPET/CT, single photon emission computed tomography (SPECT)/MRI,SPECT/CT, PET/electrical impedance tomography (EIT), SPECT/opticalimaging systems, and/or SPECT/EIT systems. Further, in addition tomedical imaging, embodiments of the systems and methods discussed hereinmay also be used in pharmacological and pre-clinical research for thedevelopment and evaluation of innovative tracer compounds.

Additionally, it may be noted that the embodiments of image dataacquisition described herein may be performed sequentially, such as byfirst obtaining PET image data followed by the acquisition of MRI data,or vice versa. Alternatively, the image data acquisition may beperformed substantially simultaneously via simultaneous acquisition ofPET and MRI data for use in simultaneous attenuation correction andimage reconstruction. An exemplary environment that is suitable forpracticing various implementations of the present disclosure isdiscussed in the following sections with reference to FIG. 1.

FIG. 1 illustrates an exemplary imaging system 100 for enhanced emissiontomography imaging. Particularly, in one embodiment, the system 100corresponds to a PET/MRI system configured to provide enhanced PETattenuation correction and PET image reconstruction using MRI data.Although, FIG. 1 illustrates a hybrid PET/MRI system, in certainembodiments, the system 100 may include independent PET and MRI systemsfor imaging a subject. Alternatively, the system 100 may include otherimaging systems such as a PET/CT, PET/EIT, PET/optical imaging,SPECT/MRI, SPECT/CT, SPECT/EIT, and/or a SPECT/optical imaging systemconfigured for enhanced emission tomography imaging of the subject usingcorresponding anatomical scan data.

Particularly, in one embodiment, the system 100 includes a scanner 102,a system controller 104, and an operator interface 106 that arecommunicatively coupled to each other over a communications network 107for imaging the subject. The communications network 107, for example,may include wired and/or wireless communication links corresponding to abackplane bus, a short-range network, a local area network, a wide areanetwork, and/or the Internet. In certain embodiments, the scanner 102performs simultaneous MR and PET imaging scans of the subject based onone or more control signals received from the system controller 104 viathe communications network 107.

To that end, in one embodiment, the scanner 102 includes a bore 108 intowhich a table 110 may be positioned for disposing the subject such as apatient 112 in a desired scanning position. Moreover, the scanner 102may also include a series of associated coils for imaging a targetvolume in the patient 112. Particularly, in one embodiment, the scanner102 includes a primary magnet coil 114, for example, energized via apower supply 116 for generating a primary magnetic field generallyaligned with the patient bore 108. The scanner 102 may further include aseries of gradient coils 118, 120 and 122 grouped in a coil assembly forgenerating accurately controlled magnetic fields, the strength of whichvary over a designated field of view (FOV) of the scanner 102.

Additionally, in certain embodiments, the scanner 102 may also include aradiofrequency (RF) coil 124 configured to generate RF pulses forexciting a gyromagnetic material that is typically bound in tissues ofthe patient 112. In one embodiment, the RF coil 124 may also serve as areceiving coil. Accordingly, the RF coil 124 may be operationallycoupled to transmit-receive circuitry 126 in passive and/or active modesfor receiving emissions from the gyromagnetic material and for applyingRF excitation pulses, respectively.

In certain embodiments, the system controller 104 may be configured tocontrol operation of the MR coils 118, 120, 122, and 124 for generatingthe desired magnetic fields and/or for applying the RF excitationpulses. Accordingly, in one embodiment, the system controller 104 mayinclude a pulse sequence generator 128, timing circuitry 130, and aprocessing subsystem 132 for generating and controlling imaging gradientwaveforms and RF pulse sequences employed during patient examination.Particularly, in one embodiment, the pulse sequence generator 128 may beconfigured to generate a T1-weighted, T2-weighted, T2*-weighted,susceptibility-weighted, proton density-weighted, fat selective, waterselective, and/or Dixon pulse sequences for acquiring desired MRI data.

Further, in certain embodiments, the system controller 104 may includeamplification circuitry 134 and interface circuitry 136 configured tocontrol and/or interface between the pulse sequence generator 128 andthe coils of the scanner 102. For example, the amplification circuitry134 and/or the interface circuitry 136 may be configured to drive the RFcoil 124 and amplify corresponding MRI response signals for furtherprocessing. Additionally, in certain embodiments, the amplificationand/or interface circuitry 134-136 may also be configured to amplifyresponse signals, such as electrocardiogram (ECG) signals, received fromone or more sensors (not shown) operatively coupled to the patient 112.

The amplified response signals, in turn, may be transmitted to theprocessing subsystem 132 for determining information for use in imagereconstruction. Particularly, in one embodiment, the processingsubsystem 132 demodulates, filters, and/or digitizes the receivedresponse signals for generating the processed image reconstructioninformation. Further, the processing subsystem 132 may apply selectedanalytical routines to the processed information for deriving clinicallyuseful indicators such as location of a stenosis and structural and/orfunctional parameters such as blood flow in the target volume.Accordingly, in one embodiment, the processing subsystem 132 may includedevices such as one or more application-specific processors, graphicalprocessing units (GPUs), digital signal processors (DSPs),microcomputers, microcontrollers, Application Specific IntegratedCircuits (ASICs) and/or Field Programmable Gate Arrays (FPGAs).

Further, in certain embodiments, the processing subsystem 132 stores theprocessed information and/or the clinically useful indicators in astorage repository 140. In one embodiment, the storage repository 140may also store physical and logical axis configuration parameters, pulsesequence descriptions, and/or programming routines for use by thescanner 102. Additionally, in certain embodiments, the storagerepository 140 may further store programming code for implementing oneor more algorithms capable of performing PET attenuation correctionand/or PET image reconstruction based on acquired MRI data in accordancewith an aspect of the present specification. To that end, the storagerepository 140 may include devices such as a hard disk drive, a floppydisk drive, a compact disk-read/write (CD-R/W) drive, a DigitalVersatile Disc (DVD) drive, a flash drive and/or a solid-state storagedevice.

Additionally or alternatively, in certain other embodiments, theprocessing subsystem 132 transmits the processed information and/or theclinically useful indicators to an image reconstruction unit 138 toallow reconstruction of desired images of the target volume.Particularly, the image reconstruction unit 138 may be configured toreconstruct an MR image corresponding to the target volume in thepatient 112. The target volume, for example, may correspond to one ormore biological tissues such as hepatic, neural, or cardiac tissues inthe patient 112. Alternatively, in embodiments, where the system 100 isused for non-medical imaging, the target volume may correspond to aselected region of interest in a non-biological object.

In one embodiment, the target volume may be selected by an operator 144via the operator interface 106. Additionally, the operator interface 106may allow the operator 144 to specify commands and scanning parametersvia one or more input devices 146 for use during the MRI and/or PETscan. To that end, the operator interface 106 may provide one or moreselectable and/or definable options on one or more of the input devices146 for configuring imaging parameters such as table motion, patientorientation, table orientation, and/or imaging pulse sequences. Further,in certain embodiments, the operator interface 106 may also includeoptions for requesting a desired output of MR images, PET images, and/orcorresponding diagnostic information.

Particularly, in certain embodiments, the operator interface 106includes one or more output devices 148 such as a display 150 and/or aprinter 152 for receiving the requested output information. The display150, for example, may be integrated into wearable eyeglasses, or may beceiling or cart mounted to allow the interventional practitioner 144 toobserve the reconstructed images, data derived from the images and otherrelevant information such as scanning time throughout the procedure. Inone embodiment, the display 150 may include an interactive userinterface that may also allow selection and display of scanning modes,FOV, and prior exam data. Additionally, the interactive user interfacemay allow on-the-fly access to patient data such as respiration andheart rate, scanning parameters, and/or options for selection of an ROIfor subsequent imaging.

Thus, during a medical examination, MRI allows determination ofstructural and/or functional information of the target volume that mayaid in diagnosis, prescribing treatment, and/or as complementaryinformation for studying complex biochemical processes. In certainembodiments, the structural information derived from MRI images may beused for determining attenuation coefficients for use in PET imagereconstruction. To that end, the PET data may be acquired sequentiallyand/or substantially simultaneously with the MR data acquisition.

Particularly, in one embodiment, a positron emitter or a radiotracer maybe administered to the patient 112 that targets specific tissuescorresponding to the target volume in the patient's body. Further, incertain embodiments, the system 100 includes a detector ring assembly154 that is disposed about the patient bore 108 and is configured todetect radiation events corresponding to the target volume. In oneembodiment, the detector ring assembly 154 includes multiple detectorrings that are spaced along the central axis of the system 100. Thedetector rings contain a plurality of detector modules 156, which inturn, may include a 6×6 array of individual bismuth germanate (BGO)detector crystals. Generally, the detector modules 156 may be used todetect gamma radiation emitted from the patient 112 and may producephotons in response to the detected gamma radiation.

Accordingly, in one embodiment, the array of detector modules 156 arepositioned proximate to a plurality of photomultiplier tubes (not shown)in the system 100. In certain embodiments, the photomultiplier tubes(PMTs) are configured to produce analog signals when a scintillationevent occurs at one of the detector modules 156. Specifically, the PMTsmay be configured to produce analog signals when a gamma ray emittedfrom the patient is received by one of the detector modules 156.Further, the system 100 may also include a set of acquisition circuits158 that may be configured to receive the analog signals and generatecorresponding digital signals. In one embodiment, the digital signalsare indicative of a location and energy associated with a detectedradiation event, and thus, may be used during PET image reconstruction.

In certain embodiments, the system 100 further include a dataacquisition system (DAS) 160 that may be configured to periodicallysample the digital signals produced by the acquisition circuits 158. TheDAS 160, in turn, may include one or more event locator circuits 162configured to assemble information corresponding to each valid radiationevent into an event data packet. The event data packet, for example, mayinclude a set of digital numbers that may accurately indicate a time ofthe radiation event and a position of the detector crystal that detectedthe radiation event.

Further, in one embodiment, the event locator circuits 162 communicatethe assembled event data packets to a coincidence detector 164 fordetermining coincidence events. Particularly, the coincidence detector164 determines coincidence event pairs if time and location markers intwo event data packets are within certain designated thresholds. By wayof example, in one embodiment, the coincidence detector 164 may beconfigured to identify a coincidence event pair if time markers in twoevent data packets are within 12 nanoseconds of each other and if thecorresponding locations lie on a straight line passing through a fieldof view (FOV) across the patient bore 108.

In certain embodiments, the system 100 stores the determined coincidenceevent pairs in the storage repository 140. The storage repository 140,in one embodiment, includes a sorter 166 to sort the coincidence events,for example, in a 3D projection plane format using a look-up table.Particularly, the sorter 166 orders the detected coincidence event datausing one or more parameters such as radius or projection angles forefficient storage. In one embodiment, the processing subsystem 132processes the stored data to determine time-of-flight (TOF) and/ornon-TOF information. The TOF information may allow the PET/MRI system100 to estimate a point of origin of the electron-positron annihilationwith greater accuracy, thus improving event localization.

In certain embodiment, the event localization information may be used tofurther enhance the quality of PET images reconstructed by the imagereconstruction unit 138. Particularly, the event localizationinformation may be used to reconstruct a PET activity or emission map(or image) that defines a spatial distribution of a radiotracer in thepatient body based on the emitted 511 keV photons measured by thedetector modules 156. Typically, the emitted photons travel throughdifferent regions of the patient body or extra-patient components suchas tissue, lungs, air, beds and/or MR coils, and thus, experiencedifferent attenuations.

Certain conventional imaging approaches entail correcting theattenuation values in the activity maps using MRI information determinedvia segmentation and/or atlas-based registration of the MR images.However, as previously noted, such conventional approaches may result ininaccurate attenuation correction in and/or near ambiguous image-regionssuch as bones, metal implants, air, and lungs due to insufficientvisualization of these regions in the MR images.

Unlike such conventional approaches, the image reconstruction unit 138jointly estimates attenuation in the sinogram space and an emissionactivity map from PET and MRI data. Particularly, in one embodiment,attenuation factors corresponding to lines of response (LORs) passingthrough distinctive image-regions such as fat and water that can bereliably determined from MR scan data are calculated byforward-projecting known attenuation coefficient values along the LORs.Further, attenuation factors that correspond to lines of response (LORs)passing through the ambiguous image-regions that are indistinguishablein the MR images may be selectively updated using PET emission scandata.

Thus, the image reconstruction unit 138 allows for simultaneousestimation of both the complete emission activity map and anyundetermined attenuation factors in projection-space corresponding tothe ambiguous image-regions using the determined attenuation factors.The accurately determined PET activity map and the projection-spaceattenuation factors, in turn, allow reconstruction of high quality TOFand/or non-TOF PET images and/or provide the operator 144 withcorresponding diagnostic information. Particularly, in one embodiment,the PET images and the corresponding diagnostic information may becommunicated to the operator 144 via one or more of the output devices148, such as the display 150, the printer 152, and/or an audio-videodevice coupled to the operator interface 106. Communicating the enhancedattenuation-corrected PET images and/or diagnostic information allows amedical practitioner to assess a health condition of the patient 112with greater accuracy.

It may be noted that the specific arrangements depicted in FIG. 1 areexemplary. Further, the system 100 may be configured or customized foradditional functionality, different imaging applications and scanningprotocols. Accordingly, in certain embodiments, the system may becoupled to multiple displays, printers, workstations, and/or similardevices located either locally or remotely, for example, within aninstitution or hospital, or in an entirely different location via theone or more configurable wired and/or wireless networks 107 such as theInternet, cloud computing, and/or virtual private networks.

By way of example, in one embodiment, the system 100 may be coupled to apicture archiving and communications system (PACS) to store theresulting MR and attenuation-corrected PET images. Additionally, thesystem 100 may be coupled to other remote systems such as a radiologydepartment information system, a hospital information system, and/or toan internal or external network to allow operators at differentlocations to supply commands and parameters and/or gain access to thePET attenuation factors and/or enhanced PET images. An exemplary methodfor enhanced emission tomographic imaging using joint estimation will bedescribed in greater detail with reference to FIG. 2.

FIG. 2 illustrates a flow chart 200 depicting an exemplary method forenhanced emission tomography imaging. In the present specification,embodiments of the exemplary method may be described in a generalcontext of computer executable instructions on a computing system or aprocessor. Generally, computer executable instructions may includeroutines, programs, objects, components, data structures, procedures,modules, functions, and the like that perform particular functions orimplement particular abstract image data types.

Additionally, embodiments of the exemplary method may also be practisedin a distributed computing environment where optimization functions areperformed by remote processing devices that are linked through a wiredand/or wireless communication network. In the distributed computingenvironment, the computer executable instructions may be located in bothlocal and remote computer storage media, including memory storagedevices.

Further, in FIG. 2, the exemplary method is illustrated as a collectionof blocks in a logical flow chart, which represents operations that maybe implemented in hardware, software, or combinations thereof. Thevarious operations are depicted in the blocks to illustrate thefunctions that may be performed, for example, during the steps ofgenerating the image-space certainty map and/or determining a desiredemission image and desired values of the attenuation factors in theexemplary method. In the context of software, the blocks representcomputer instructions that, when executed by one or more processingsubsystems, perform the recited operations.

The order in which the exemplary method is described is not intended tobe construed as a limitation, and any number of the described blocks maybe combined in any order to implement the exemplary method disclosedherein, or an equivalent alternative method. Additionally, certainblocks may be deleted from the exemplary method or augmented byadditional blocks with added functionality without departing from thespirit and scope of the subject matter described herein. For discussionpurposes, the exemplary method will be described with reference to theelements of FIG. 1.

Emission tomography imaging allows for generation of two-dimensional(2D) and/or three-dimensional (3D) images that provide structural and/orfunctional information corresponding to a target volume. Accordingly,the emission images find use in study of complex biochemical processesand/or detecting disease conditions based on certain image-derivedparameters. Particularly, the image-derived parameters may be used indiagnosing and/or prescribing a suitable treatment for a patient.Accuracy of the diagnosis and/or treatment prescription, thus, dependsupon accurate reconstruction of an emission activity map, which in turn,depends upon accurate attenuation correction of the emission scan data.

Accordingly, the following description discloses an embodiment of thepresent method that allows for simultaneous estimation of activity andattenuation using emission scan data acquired from an emissiontomography system and anatomical scan data such as MRI scan datareceived from an anatomical scan system. Particularly, the presentmethod entails use of anatomical scan data in addition to the emissionscan data to determine accurate attenuation values even for regions inthe target volume that are conventionally difficult to classify usingthe anatomical scan data.

The method begins at step 202, where emission scan data and anatomicalscan data corresponding to a target volume in a subject is acquired. Theemission scan data, for example, may be acquired using an emissiontomography system such as the PET/MRI system 100 of FIG. 1. Further, theanatomical scan data may be acquired, for example, using anatomical scansystems such as an MRI system, a CT imaging system, an X-ray imagingsystem, an EIT system, and/or an optical imaging system. Moreover, incertain embodiments, the emission and anatomical scan data are acquiredusing a respiratory and/or cardiac gating pulse sequence.

Particularly, in one embodiment, a radiotracer such asFluorodeoxyglucose (FDG) may be administered to the patient foracquiring the emission scan data corresponding to a target volume. Thetarget volume may correspond to biological tissues, for example, liveror lung tissues corresponding to the patient. In certain embodiments,the PET/MRI system 100 acquires the PET emission scan data correspondingto the target volume during an estimated decay period of theradiotracer. Specifically, measured values representative of an uptakedistribution of the radiotracer in the target volume as a function oftime may be used for reconstructing desired 2D and/or 3D images of thetarget volume. Further, the reconstructed images may aid in anassessment of one or more functional and/or physiological parameters,such as blood flow, in the target volume.

To that end, in one embodiment, the acquired PET data may be storedalong with time-of-flight (TOF) information corresponding to a measureddifference in time between arrivals of each pair of gamma photons fromeach annihilation event for use in PET image reconstruction. In anotherembodiment, the PET/MRI system 100 stores the acquired PET data withoutTOF information. Particularly, in certain embodiments, the acquired PETdata may be stored in a list-mode and/or a sinogram format.

Further, in certain embodiments, the anatomical scan data may beacquired by an anatomical scan system, such as the PET/MRI system 100,before, after, and/or during the PET data acquisition. In oneembodiment, the system 100 uses Liver Acquisition with VolumeAcquisition (LAVA), LAVA flex, localizer, ultrashort echo time (UTE),zero echo time (ZTE), and/or gapped three-dimensional (3D) grid MRsequences for acquiring the MRI data. Additionally, in certainembodiments, the system 100 optimizes the MRI contrast used during theMRI data acquisition. The MRI contrast, for example, may be T1-weighted(T1w), proton density weighted (PDw), T2 weighted (T2w), and/or forsegmenting a particular tissue type and/or to avoid certain artifacts.Moreover, in some embodiments, the system 100 may also pre-process theMRI data, for example, to aid in a three-dimensional gradient linearitycorrection in resulting MR images.

Moreover, at step 204, one or more at least partial anatomical scanimages are reconstructed using the anatomical scan data. Specifically,in one embodiment, the MRI scan data may be used for reconstructingin-phase (I_(i)), out-of-phase (I_(o)), water (I_(w)), fat (I_(f)),proton density-weighted, UTE, ZTE, localizer, and/or gapped 3D gridimages. In certain embodiments, the MR data acquisition performed withina same repetition time (TR) may obtain all four images corresponding toin-phase, out-of-phase, water, and fat for a particular slice selection,and may be extended for generating MR images of the whole body of thepatient. By way of example, the system 100 may be configured toreconstruct the desired MR images corresponding to the target volumeusing Dixon and/or Iterative Decomposition of water and fat with EchoAsymmetry and Least squares estimation (IDEAL) methods.

Further, at step 206, one or more distinctive and one or more ambiguousimage-regions are identified in the one or more at least partialanatomical scan images. Typically, the anatomical scan images such asthe MR images provide anatomical information with high spatialresolution and soft tissue contrast. Accordingly, MR images provideefficient visualization or classification of water, fat, and/or softtissues. Thus, these regions correspond to distinctive image-regions.Particularly, as used herein, the term “distinctive image-regions” isused to refer to the image-regions that have classifiable tissue typesand for which a PET attenuation coefficient may be reliably determined.

However, MRI provides poor visualization of proton deficient regionssuch as bone, metal, air, and/or lungs even though these materialsexhibit different PET attenuation values. Thus, the MR image may includeone or more ambiguous image-regions corresponding to a location of suchproton deficient materials in the target volume. Moreover, in oneembodiment, truncation of a field of view of the MRI system may alsoresult in low or no MRI signals in the truncated regions, which may berepresented as ambiguous image-regions in the MR image. Accordingly, asused herein, the term “ambiguous image-regions” is used to refer toimage-regions having uncertain classification and for which a PETattenuation coefficient may not be reliably determined.

According to certain aspects of the present specification, in certainembodiments, the MR images may undergo bias field correction beforeidentifying the one or more distinctive and/or ambiguous image-regions.Additionally, segmentation, atlas-based registration, machine learning,and/or MRI pulse sequences such as LAVA flex, IDEAL MRI, ultra-shortecho time (UTE), and/or zero echo time (ZTE) may be used to identify oneor more distinctive and/or ambiguous image-regions in the MR images. Forexample, use of the LAVA flex sequence or the IDEAL MRI pulse sequencemay allow for identification of distinctive water and fat image-regionsin the MR images with a desired confidence level. Alternatively, the MRimages may be segmented, for example, based on thresholding, partialdifferential equations (PDE), atlas-based or template-basedregistration, a multi-scale model, machine learning, region growing,and/or active contour methods to identify the distinctive and/orambiguous image-regions.

Particularly, in certain embodiments, the MR images may be segmentedbased on threshold values which, for example, are determined using afirst local minimum of a histogram generated using voxel valuescorresponding to one or more of the MR images and/or one or more regionsof interest in the MR images. Further, in one embodiment, regions whosevoxel values are smaller than the threshold values may be determined asambiguous image-regions in the MR images. In another embodiment, prioranatomical knowledge may be used in identifying the one or moreambiguous image-regions. For example, regions that are likely to containvertebrae may be identified as ambiguous image-regions based on alocation of lungs in the target volume that may be determined usingsegmentation. However, in the present method, the ambiguousimage-regions need not necessarily correspond to an organ or aclassifiable segment boundary, but rather may correspond to voxels thatcannot be accurately identified with a desired confidence level. Thus,in certain embodiments, boundary regions between identified segments maybe labeled as ambiguous image-regions due to an uncertainty caused bypartial volume effects.

Further, in some embodiments, the distinctive and/or ambiguousimage-region in an MR image may be identified based on a dataacquisition protocol or the field of view (FOV) of the scanner. By wayof example, regions truncated due to a small FOV of the system 100,and/or regions reconstructed with insufficient image quality may beidentified as the ambiguous image-regions. Moreover, in one embodiment,regions in the MR images, where the MRI data is unavailable orundetermined due to use of an MRI localizer scan or MRI scan on 3Dgapped grids may also be identified as ambiguous image-regions.

Further, at step 208, an image-space certainty map is generated. Theimage-space certainty map represents a degree of confidence in theattenuation coefficient or attenuation value assigned to one or moreselected voxels in one or more at least partial anatomical scan images.In certain embodiments, the image-space certainty map may be binary.Accordingly, in one embodiment, if there is sufficient confidence in theattenuation value assigned to a voxel, the corresponding voxel of theimage-space certainty map is set to one, or else the voxel in theimage-space certainty map is set to zero.

For example, voxels corresponding to the distinctive image-regions inthe MR image may be assigned a value of one, whereas the voxelscorresponding to the ambiguous image-regions may be set to zero. Incertain embodiments, if a voxel belongs to both the distinctiveimage-regions and the ambiguous image-regions, then the correspondingvoxel of the image-space certainty map may be set to zero. In certainother embodiments, voxels in the image-space certainty map may beassigned continuous values from a selected range such that voxelscorresponding to distinctive image-regions are assigned, for example,higher values in the image-space certainty map, whereas other pixels areassigned lower values.

Additionally, at step 210, a preliminary attenuation map is generatedbased on the one or more at least partial anatomical images. In certainembodiments, known attenuation values are assigned to the one or moredistinctive image-regions and selected attenuation values are assignedto the one or more ambiguous image-regions identified at step 206. Inone embodiment, the known attenuation values corresponding to thedistinctive image-regions such as fat, water, and soft tissues may bedetermined from clinically prescribed lookup tables. The lookup tablesmay store one or more correlations between different tissues typesand/or constituent materials in the distinctive image-regions and theircorresponding attenuation values, thus allowing for appropriateassignment of attenuation values to each of the distinctiveimage-regions in the MR image.

Further, in certain embodiments, selected attenuation values may beassigned to the ambiguous image-regions in the at least partial MRimages. Generally, the attenuation map represents a distribution oflinear attenuation coefficients or attenuation values corresponding toemission photons. Accordingly, in one embodiment, the attenuation map isgenerated based on the known and selected attenuation values assigned toeach pixel and/or voxel in distinctive and/or ambiguous image-regionidentified in the at least partial MR images. In certain embodiments,the attenuation values corresponding to the preliminary attenuation mapmay be selected randomly from attenuation values corresponding to aselected list of ambiguous materials in the stored lookup table.Alternatively, the attenuation values may be selected based onanatomical context and/or predetermined patient information such aspresence of implants. Accordingly, in certain embodiments, thepreliminary attenuation map may be correlated with the at least partialMR images, for example, based on image segmentation, an anatomicalatlas-based image registration, a predetermined template-based imageregistration, and/or via machine learning that employs MR images, and/ora bilinear transformation using X-ray CT images.

Moreover, at step 212, one or more of a selected set of attenuationfactors in a projection space corresponding to the emission scan dataare initialized based on the preliminary attenuation map. The selectedset of attenuation factors may correspond to all of the attenuationfactors, or one or more subsets of the attenuation factors selected foruse in different imaging stages and/or imaging goals. In one embodiment,a relationship between the preliminary image-space attenuation map andattenuation factors in the sinogram space is determined using theBeer-Lambert law. Specifically, given the preliminary attenuation mapgenerated at step 210, initial attenuation factors may be determined bycalculating the forward-projection of the preliminary attenuation mapfollowed by computing an exponential function of negativeforward-projection values.

Additionally, at step 214, a projection-space certainty map thatrepresents a level of confidence corresponding to the value of one ormore of the selected set of attenuation factors is determined based onthe image-space certainty map generated at step 208. Particularly, inone embodiment, a binary projection-space certainty map may becalculated from the binary image-space certainty map. To that end, thebinary image-space certainty map is negated such that values of voxelsare changed from one to zero and vice versa. The negated image-spacecertainty map is then forward-projected. Further, the binaryprojection-space certainty map is constructed such that a bin in theprojection-space certainty map is set to zero if a correspondingforward-projection value is non-zero. Alternatively, the bin in theprojection-space certainty map is set to one if a correspondingforward-projection value is zero. Thus, if a LOR passes through a voxelcorresponding to an ambiguous image-region whose image-space certaintyvalue is zero, then the corresponding projection-space certainty valueis set to zero. Such LORs, thus, also correspond to ambiguousprojection-regions. Alternatively, if a LOR passes only through voxelscorresponding to a distinctive image-region whose image-space certaintyvalue is one, then the corresponding projection-space certainty value isset to one to generate the binary projection-space certainty map. SuchLORs, thus, also correspond to distinctive projection-regions.

Further, in certain other embodiments, a continuous-valuedprojection-space certainty map may be determined by calculating aforward-projection of the binary or continuous-valued image-spacecertainty map. Alternatively, the continuous-valued projection-spacecertainty map may be determined by normalizing the forward-projectionvalues, which are weighted sums of the image-space certainty map valuesgenerated at step 208.

Moreover, at step 216, an emission image corresponding to the targetvolume is initialized. In one embodiment, the emission image may beinitialized based on a uniform image. Alternatively, the emission imagemay be initialized based on an image reconstructed by filteredbackprojection (FBP) or ordered subset expectation maximization (OSEM)using the preliminary attenuation map generated at step 210.

Further, at step 218, the emission image and one or more of the selectedset of attenuation factors corresponding to at least the one or moreambiguous projection-regions are iteratively updated based on theemission scan data, the projection-space certainty map, the initialemission image, and the one or more initial attenuation factors. In oneembodiment, the initial emission image and the projection-spaceattenuation map may be updated alternately until one or more selectedtermination criteria are satisfied. For example, in one iteration, theinitial emission image may be updated based on the initial attenuationfactors. Further, in a subsequent iteration, the initial attenuationfactors may be updated based on the updated emission image.

Particularly, as previously noted, the emission image may be initializedusing a uniform image and/or a predetermined emission image.Subsequently, the initialized emission image may be updated, forexample, using a maximum likelihood (ML), penalized likelihood (PL), orBayesian maximum a posteriori (MAP) framework. Particularly, in certainembodiments, the emission image may be iteratively updated, for example,using an ordered subset expectation maximization (OSEM), expectationmaximization (EM), block sequential regularized expectation maximization(BSREM), and/or preconditioned conjugate gradient (PCG)-based method.

Additionally, regularization of the iterative update may be performedusing one or more penalty functions. In one example, penalty functionssuch as quadratic penalties, Huber penalties, generalized Gaussianpenalties, and/or relative difference penalties may be used to penalizedifferences between values of neighboring voxels. Alternatively, penaltyfunctions that include prior distributions of attenuation coefficientssuch as multi-modal or Gaussian mixture distributions may be used topenalize deviations from a selected or a reference attenuation map. Incertain embodiments, the iterative update may also entail use of sums ofvoxel values that are available in certain regions of the emission imageas constraints during the update of the emission image.

Similarly, the attenuation factors corresponding to the ambiguousprojection-regions may be initialized using the initial attenuationfactors determined at step 212 and/or using predetermined attenuationfactors. Further, the attenuation factors may be updated, for example,using OSEM, EM, ordered subset separable paraboloidal surrogate (OSSPS),and/or PCG-based methods.

Additionally, the iterative update of the attenuation factors may alsoemploy regularization or penalty functions that penalize differencesbetween neighboring bins in certain determined directions. Thesedirections, for example, include transaxial radial, transaxial(azimuthal) angular, axial and polar angular directions. In certainembodiments, the iterative update entails calculating upper bounds forone or more selected attenuation factors based on the preliminaryattenuation map, the image-space certainty map, and the projection-spacecertainty map. Further, one or more inequality constraints based on theinitial attenuation factors may be applied during update of theattenuation factors.

Alternatively, a parametric model and one or more parameters thatparameterize the parametric model are determined to represent theselected attenuation factors. By way of example, the parametric modelincludes an inverse Fourier rebinning or inverse single slice rebinning.Further, the parameters include one or more attenuation factorscorresponding to direct plane LORs. Accordingly, in one embodiment, theparameters corresponding to the initialized attenuation factors areinitialized. Moreover, during the iterative update, the attenuationfactors corresponding to oblique planes are parameterized by theattenuation factors corresponding to direct planes through inverseFourier rebinning or inverse single-slice rebinning. However, only thedirect-plane attenuation factors may be updated. As a number ofdirect-plane attenuation factors is generally known to be much smallerthan that of oblique-plane attenuation factors, a number of unknowns toestimate during the iterative updates is substantially reduced. It maybe noted that, in certain embodiments, the initial attenuation factorsand an initial emission image may be iteratively updated, for example,until the selected termination criteria are satisfied.

Accordingly, at step 220, it may be determined if one or moretermination criteria are satisfied for terminating the iterative updateof the emission image and the attenuation factors. The terminationcriteria, for example, includes a verification step to check if adesired number of iterations is performed and/or if a determineddifference between the current and the previous iterate is smaller thana predetermined value.

If the termination criteria are not satisfied at step 220, controlpasses back to step 218 and the iterative update of the emission imageand the attenuation factors continue. However, if at step 220, thetermination criteria are satisfied, control passes to step 222.

Particularly, at step 222, a desired emission image and/or desiredvalues of the one or more attenuation factors are determined based onthe iteratively updated emission image and the iteratively updatedattenuation factors. In one embodiment, the emission image and theattenuation factors determined during the final iteration at step 220are used as the desired emission image and the desired attenuationfactors. Alternatively, the desired PET emission image may bere-reconstructed from the PET emission scan data using the attenuationfactors determined during the final iteration of step 220 and emissiontomography image reconstruction algorithms. The emission tomographyimage reconstruction algorithms, for example, include OSEM, BSREM, PCG,SPS and its ordered subsets (OS) version and/or De Pierro's modifiedexpectation maximization and its OS version.

An exemplary mathematical description of the present method describedwith reference to FIG. 2 is described herein.

In one embodiment, TOF PET emission sinogram data y_(ik), acquired atstep 202, may be modeled as independent Poisson random variables thatmay be represented using equation (1).

E[y _(ik) ]=y _(ik)(λ,α)≡α_(i) s _(ik)(λ)+r _(ik)  (1)

-   -   for i=1, . . . , N_(b) and k=1, N_(t)        where i and k corresponds to LOR index and TOF bin index,        respectively, N_(b), N_(t), and N_(v) correspond to number of        sinogram bins, TOF bins, and image voxels, respectively, λ        corresponds to an N_(v) by 1 column vector representing the        emission image, α corresponds to an N_(b) by 1 column vector        whose elements α_(i) represent attenuation factors, s_(ik)        corresponds to an unattenuated TOF forward projection of the        emission image, and r_(ik) corresponds to mean contribution of        randoms and scatters.

In one embodiment, the unattenuated TOF forward projection may berepresented using equation (2).

s _(ik)(λ)=Σ_(j) a _(ij) ^(k)λ_(j)  (2)

where a_(ij) ^(k) corresponds to a TOF forward projector includingnormalization and detector blurring.

In a presently contemplated embodiment, the emission image λ and theattenuation factors α_(i) may be estimated given the TOF PET sinogramdata y_(ik), and values corresponding to the background contributionsr_(ik) that may be estimated using predetermined information.Particularly, the attenuation factors may be represented using equation(3).

α_(i)=exp(−Σ_(j) g _(ij)μ_(j))  (3)

where g_(ij) corresponds to a non-TOF geometric forward projector, whichmodels the length of LOR i that intersects with voxel j, and μcorresponds to an N_(v) by 1 column vector representing the attenuationmap, whose elements μ_(j) correspond to a linear attenuation coefficientof voxel j for 511 keV photons.

Further, the non-TOF geometric forward projector may be implementedusing Siddon's method or distance-driven projectors. In an embodimentwhere attenuation coefficients are known for a certain set J of voxelsin the attenuation map, for example, represented using equation (4),

μ_(j)=μ_(j) ^(known) for jεJ  (4)

the attenuation factors may be rewritten using equations (5), (6) and(7).

α_(i)=γ_(i) exp(−Σ_(jεJ′) g _(ij)μ_(j))  (5)

where γ_(i)≡exp(−Σ_(jεJ) g _(ij)μ_(j) ^(known)) and  (6)

J′≡{j=1, . . . , N _(v) : j does not belong to J}  (7)

where J corresponds to the distinctive image-regions, J′ corresponds tothe ambiguous image-regions as identified at step 206, and μ_(j)^(known) may be determined in step 210. It holds that α_(i)≦γ_(i) sinceg_(ij)≧0 and μ_(j)≧0.

Accordingly, in one embodiment, a binary image-space certainty map maybe determined using equation (8), as generated at step 208.

ζ_(j)=1 if jεJ and ζ_(j)=0 if jεJ′  (8)

In an embodiment, where the LOR i passes only through voxels with knownattenuation coefficients in J, the corresponding attenuation factorα_(i) has a known value γ_(i). Thus, the LOR i belongs to thedistinctive projection-regions. Accordingly, the set of such LORs may berepresented, for example, using equation (9).

Ī≡{i: For all j, if g _(ij)≠0, then jεJ}  (9)

where Ī represents the distinctive projection-regions.

Further, the attenuation factors may be represented using equation (10).

α_(i)=γ_(i) if iεĪ  (10)

where γ_(i) is calculated using equation (6) for iεĪ and may bedetermined as described in step 212.

Alternatively, when iεI≡{i: there exists j such that g_(ij)≠0 and j doesnot belong to J}, α_(i) is unknown and may be estimated. Here, Irepresents the ambiguous projection-regions. Thus, a binaryprojection-space certainty map ξ, such as the map generated at step 214,may be determined using equation (11).

ξ_(i)=1 if iεĪ and ξ_(i)=0 if iεI  (11)

Further, a penalized-likelihood (PL) objective function for use in theiterative update may be represented using equation (12).

Φ(λ,α)=Σ_(i,k) y _(ik) log y _(ik)(λ,α)− y _(ik)(λ,α)−R _(λ)(λ)−R_(α)(α)  (12)

where R_(λ)(λ) corresponds to the regularization or penalty function forthe emission image and R_(λ)(α) corresponds to the regularization orpenalty function for the attenuation factors. It may be noted that whenthe penalty functions are all zero, the objective function correspondsto the (log) likelihood function.

Maximizing the PL objective function with respect to the activity imageλ and the attenuation factors α subject to constraints that λ_(j)≧0 forall j, α_(i)≦γ_(i) for iεI, and α_(i)=γ_(i) for iεĪ leads to estimatedemission image and attenuation factors. As previously noted withreference to step 218, the emission image λ and attenuation factors αmay be alternatively updated, for example, using OSEM or OSSPS andmodified EM, respectively.

The resulting emission images and/or the attenuation factors allow foraccurate quantitation of tracer uptake, for example, for detecting andstaging cancers and monitoring response to treatment. FIGS. 3-17 depictcertain simulated images that are indicative of a performance of anexemplary implementation of the method described with reference to FIG.2.

Particularly, FIG. 3 depicts a transaxial slice 300 of a true emissionimage, whereas FIG. 4 depicts a coronal slice 400 of the true emissionimage corresponding to a target volume. Further, FIGS. 5 and 6 depicttransaxial and coronal slices 500 and 600, respectively, correspondingto a true attenuation map. In the exemplary implementation, the trueemission image and the true attenuation map depicted in FIGS. 3-6 areused to generate simulated PET emission data such as the emission scandata that is acquired and received in step 202. Moreover, FIG. 7 depictsan exemplary image 700 that depicts a plurality of the true attenuationfactors.

Further, FIGS. 8 and 9 depict transaxial and coronal slices 800 and 900of a preliminary attenuation map, such as the map determined at step210, from the MRI data that is acquired at step 202 and reconstructed atstep 204, respectively. As shown in FIGS. 8-9, the transaxial andcoronal slices 800 and 900 include ambiguous image-regions 802 and 902,respectively, that are identified at step 206. The ambiguousimage-regions 802 and 902 correspond to regions where image-spacecertainty value is zero as determined at step 208 and the depictedattenuation coefficient is incorrect. Similarly, FIG. 10 depicts animage 1000 that depicts initial attenuation factors, such as theattenuation factors calculated at step 212 using the preliminaryattenuation map. Additionally, FIG. 10 depicts ambiguousprojection-regions 1002 where the projection-space certainty value iszero, for example, as determined at step 214.

Further, FIGS. 11 and 12 depict transaxial and the coronal slices 1100and 1200, respectively, of an emission image reconstructed from noisyTOF emission scan data using the true attenuation map, such as theattenuation map depicted in FIGS. 5 and 6, generated using OSEM. FIGS.11-12, thus, correspond to reference reconstructions of the targetvolume. Additionally, FIGS. 13-14 depict transaxial and coronal slices1300 and 1400, respectively, of an emission image reconstructed from thenoisy TOF emission scan data using the preliminary attenuation map asshown in FIGS. 8 and 9. A comparison of FIGS. 13-14 and 11-12 shows amismatch in the visualization of the target volume. Particularly, thecomparison indicates that FIGS. 13-14 include image artifacts caused dueto incorrect attenuation coefficients corresponding to regions 802 and902.

Further, FIGS. 15 and 16 depict transaxial and coronal slices 1500 and1600, respectively, of an emission image reconstructed using anembodiment of the present method described with reference to FIG. 2. Asevident from the depictions of FIGS. 15 and 16, the image artifactsdepicted in FIGS. 13-14 are not observed in FIGS. 15-16. In particular,the FIGS. 15-16 appear similar to the reference reconstructions of thetarget volume depicted in FIGS. 11-12, thereby indicating accuracy ofthe attenuation factors determined using the present method. Further,FIG. 17 depicts the final and/or updated attenuation factors obtainedusing the method described with reference to FIG. 2.

Embodiments of the present systems and methods, thus, allow simultaneousestimation of the PET emission images and PET attenuation in thesinogram space based on PET data and MRI data. Particularly, embodimentsdescribed herein provide an improved imaging workflow that combinesinformation from both PET and MRI scans to alleviate the challenges inattenuation correction and/or image reconstruction using only PET oronly MRI data. Particularly, the present systems and methods allow foraccurate identification of conventionally ambiguous image-regions suchas those including bone, metal, air, lungs, truncated regions, and/orregions imaged with insufficient quality.

Moreover, estimating a subset of attenuation factors in sinogram spaceprovides greater computational efficiency and robustness to inaccurateMR information resulting from erroneous segmentation or unavailableinformation. The present systems and methods, thus, allow for accuratequantitation for PET/MRI data by accurate attenuation correction andPET/MRI image reconstruction. Although the present description is drawnto PET imaging, embodiments of the present systems and methods may alsoapply to SPECT imaging where SPECT emission activity and SPECTattenuation maps may be reconstructed using SPECT projection data andMR, CT, EIT, and/or optical images.

It may be noted that the foregoing examples, demonstrations, and processsteps that may be performed by certain components of the presentsystems, for example by the system controller 104, the processingsubsystem 132, and the image processing unit 138 of FIG. 1, may beimplemented by suitable code on a processor-based system. To that end,the processor-based system, for example, may include a general-purposeor a special-purpose computer. It may also be noted that differentimplementations of the present disclosure may perform some or all of thesteps described herein in different orders or substantiallyconcurrently.

Additionally, the functions may be implemented in a variety ofprogramming languages, including but not limited to Ruby, HypertextPreprocessor (PHP), Perl, Delphi, Python, C, C++, or Java. Such code maybe stored or adapted for storage on one or more tangible,machine-readable media, such as on data repository chips, local orremote hard disks, optical disks (that is, CDs or DVDs), solid-statedrives, or other media, which may be accessed by the processor-basedsystem to execute the stored code.

Although specific features of various embodiments of the presentdisclosure may be shown in and/or described with respect to somedrawings and not in others, this is for convenience only. It is to beunderstood that the described features, structures, and/orcharacteristics may be combined and/or used interchangeably in anysuitable manner in various embodiments, for example, to constructadditional assemblies and imaging methods.

While only certain features of the present disclosure have beenillustrated and described herein, many modifications and changes willoccur to those skilled in the art. It is, therefore, to be understoodthat the appended claims are intended to cover all such modificationsand changes as fall within the true spirit of the invention.

1. A method for imaging a subject, comprising: receiving emission scandata and anatomical scan data corresponding to the target volume in thesubject; reconstructing one or more at least partial anatomical scanimages using the anatomical scan data; generating an image-spacecertainty map that represents a level of confidence corresponding to anattenuation coefficient for one or more selected voxels in the one ormore at least partial anatomical scan images; generating a preliminaryattenuation map based on the one or more at least partial anatomicalscan images; initializing one or more of a selected set of attenuationfactors in a projection-space corresponding to the emission scan databased on the preliminary attenuation map; generating a projection-spacecertainty map corresponding to the emission scan data that represents alevel of confidence corresponding to a value of one or more of theselected set of attenuation factors based on the image-space certaintymap; initializing an emission image corresponding to the target volumein the subject; iteratively updating one or more of the selected set ofattenuation factors and the emission image based on the emission scandata, the projection-space certainty map, the initial attenuationfactors, the initial emission image, or combinations thereof; anddetermining a desired emission image, desired values of the attenuationfactors, or a combination thereof, based on the iteratively updatedattenuation factors and the iteratively updated emission image.
 2. Themethod of claim 1, further comprising acquiring the emission scan datausing an emission tomography system and the anatomical scan data usingan anatomical scan system that is operatively coupled to the emissiontomography system.
 3. The method of claim 1, wherein the emission scandata comprises positron emission tomography (PET) emission scan data,time-of-flight (TOF) PET emission scan data, single photon emissioncomputed tomography (SPECT) emission scan data, or combinations thereof,and wherein the anatomical scan data comprises magnetic resonance (MR)imaging scan data, X-ray computed tomography (CT) scan data, X-ray data,optical data, electrical impedance tomography (EIT) data, ultrasoundscan data, or combinations thereof.
 4. The method of claim 1, whereinthe one or more at least partial anatomical scan images comprise one ormore MR images, the one or more MR images comprising fat and waterimages, in-phase and out-of-phase images, a proton density weightedimage, an ultrashort echo time (UTE) image, a zero echo time (ZTE)image, a localizer scan image, an image determined on a gapped 3D grid,or combinations thereof.
 5. The method of claim 1, wherein generatingthe preliminary attenuation map comprises: segmenting the one or more atleast partial anatomical scan images into a plurality of regions andassigning selected attenuation coefficients to the plurality of regions;correlating the one or more at least partial anatomical scan images tocorresponding attenuation maps using one or more mathematicaltransformations, atlas-based image registration, template-based imageregistration, or combinations thereof.
 6. The method of claim 1, whereininitializing the attenuation factors comprises calculating aforward-projection of the preliminary attenuation map.
 7. The method ofclaim 1, wherein generating the image-space certainty map comprises:identifying one or more distinctive image-regions, and one or moreambiguous image-regions in the one or more at least partial anatomicalscan images; determining a level of confidence corresponding to anattenuation coefficient for each voxel in the one or more distinctiveimage-regions, the one or more ambiguous image-regions, or combinationsthereof; and generating the image-space certainty map based on thedetermined confidence levels.
 8. The method of claim 7, whereinidentifying the one or more distinctive image-regions, and the one ormore ambiguous image-regions comprises segmenting the one or more atleast partial anatomical scan images using thresholding, a partialdifferential equation, an atlas-based or template-based registration, amulti-scale model, machine learning, region growing, an active contourmodel, or combinations thereof.
 9. The method of claim 7, wherein theone or more distinctive image-regions comprise fat, water, air, lung, orcombinations thereof, and wherein the one or more ambiguousimage-regions comprise one or more voxels having image intensitieslesser than a selected value, one or more voxels determined using prioranatomical knowledge, or combinations thereof.
 10. The method of claim7, wherein the one or more ambiguous image-regions comprise regions thatare truncated during imaging due to a small field of view correspondingto the anatomical scan imaging system, regions in the one or more atleast partial anatomical scan images that are not reconstructed withdesired quality, or combinations thereof.
 11. The method of claim 1,wherein generating the projection-space certainty map comprises:calculating a forward-projection of the image-space certainty map; andgenerating the projection-space certainty map based on the calculatedforward-projection.
 12. The method of claim 1, wherein iterativelyupdating the one or more selected attenuation factors and the emissionimage comprises selectively updating the attenuation factorscorresponding to one or more ambiguous projection-regions determined bythe projection-space certainty map.
 13. The method of claim 1, whereiniteratively updating the one or more selected attenuation factors andthe emission image comprises: updating the initial emission image basedon the initial attenuation factors, and updating the initial attenuationfactors based on the updated emission image; and alternating betweenupdating the emission image based on the updated attenuation factors inone iteration and updating the attenuation factors based on the updatedemission image in a subsequent iteration.
 14. The method of claim 1,further comprising: calculating upper bounds for one or more of theselected set of attenuation factors based on the preliminary attenuationmap, the image-space certainty map, the projection-space certainty map,or combinations thereof; and imposing one or more inequality constraintsbased on the upper bounds when iteratively updating one or more of theselected set of attenuation factors.
 15. The method of claim 1, furthercomprising: determining a parametric model to represent one or more ofthe selected set of attenuation factors, and one or more parameters thatparameterize the parametric model; initializing the parameterscorresponding to the initialized attenuation factors; and iterativelyupdating the parameters and the emission image based on the emissionscan data, the projection-space certainty map, or combinations thereof.16. The method of claim 15, wherein the parametric model comprisesinverse Fourier rebinning or inverse single slice rebinning, and whereinthe parameters comprise one or more attenuation factors corresponding todirect plane lines of response.
 17. An imaging system for imaging asubject, comprising: an emission tomography system configured to acquireemission scan data corresponding to the subject; an anatomical scansystem operatively coupled to the emission tomography system andconfigured to acquire anatomical scan data corresponding to the subject;a processing subsystem operationally coupled to one or more of theanatomical scan system and the emission tomography system, wherein theprocessing subsystem is configured to: reconstruct one or more at leastpartial anatomical scan images using the anatomical scan data; generatean image-space certainty map that represents a level of confidencecorresponding to an attenuation coefficient for one or more selectedvoxels by processing the one or more at least partial anatomical scanimages; generate a preliminary attenuation map based on the one or moreat least partial anatomical scan images; initialize one or more of aselected set of attenuation factors in a projection-space correspondingto the emission scan data based on the preliminary attenuation map;generate a projection-space certainty map corresponding to the emissionscan data that represents a level of confidence corresponding to thevalue of one or more of the selected set of attenuation factors based onthe image-space certainty map; initialize an emission imagecorresponding to the target volume in the subject; iteratively updateone or more of the selected attenuation factors and the emission imagebased on the emission scan data, the projection-space certainty map, theinitial attenuation factors, the initial emission image, or combinationsthereof; and determine a desired emission image, desired values of theattenuation factors, or a combination thereof, based on the iterativelyupdated attenuation factors and the iteratively updated emission image.18. The imaging system of claim 17, wherein the emission tomographysystem comprises a PET scanner, a SPECT scanner, a dual head coincidenceimaging system, or combinations thereof, and wherein the anatomical scansystem comprises a CT scanner, a magnetic resonance imaging (MRI)system, an X-ray system, an optical tomography system, an EIT imagingsystem, or combinations thereof.
 19. The imaging system of claim 17,wherein the imaging system is an MRI system, a PET system, a SPECTsystem, a CT system, an X-ray system, a PET-CT system, a PET-MRI system,a SPECT-CT system, and the SPECT-MRI system, the PET-x-ray system,SPECT-x-ray system, or combinations thereof.
 20. A non-transitorycomputer readable medium that stores instructions executable by one ormore processors to perform a method for imaging, comprising: receivingemission scan data and anatomical scan data; reconstructing one or moreat least partial anatomical scan images using the anatomical scan data;generating an image-space certainty map that represents a level ofconfidence corresponding to an attenuation coefficient for one or moreselected voxels in the one or more at least partial anatomical scanimages; generating a preliminary attenuation map based on the one ormore at least partial anatomical scan images; initializing one or moreof a selected set of attenuation factors in a projection-spacecorresponding to the emission scan data based on the preliminaryattenuation map; generating a projection-space certainty mapcorresponding to the emission scan data that represents a level ofconfidence corresponding to the value of one or more of the selected setof attenuation factors based on the image-space certainty map;initializing an emission image corresponding to the target volume in thesubject; iteratively updating one or more of the selected set ofattenuation factors and the emission image based on the emission scandata, the projection-space certainty map, the initial attenuationfactors, the initial emission image, or combinations thereof; anddetermining a desired emission image, desired values of the attenuationfactors, or a combination thereof, based on the iteratively updatedattenuation factors and the iteratively updated emission image.